The field of the present invention relates to a robotic system and robotic end-effector, and more particularly to one capable of high speed manipulation of objects with variable or undefined shape, structure, or size.
In the realm of robotic pick-and-place applications there has been a central focus on performing well-defined, repeatable tasks. This paradigm is fundamentally predictable and specific. Classical computation is adept at processing a precise list of instructions. As a result, technologies have been developed for a narrow range of applications allowing for the interaction with the real world. One such subset is robotic end-effectors for manipulation of objects. An example may be found in food handling applications. Although small variations may exist, the rules for any given operation tend to be very precise and the target object is well-defined. Pick-and-place grasping can be found in numerous industries, but commodity-based grasping is typically designed to leave no trace of handling. Applications exist where destructive methods of grasping can be utilized, one such field is waste handling.
Increases in computation power has led to the expansion of deep learning algorithms. In this paradigm the computer program is much more abstract and the inputs are no longer discrete, such as image recognition. Advancements in this field have numerous industrial applications. One such industry is recycling, the sorting of recycled materials. The nature of recycling is unpredictable with materials varying largely by region and have extreme variations even within that subset. The mechanical component of the robotic system is becoming a limiting factor of these robotic systems. The present inventors have recognized that similar to the shift in software, mechanical technologies need be developed to interact with objects of unpredictable size, shape, orientation, and composition.